Literature DB >> 26120221

Evaluation of geostatistical estimators and their applicability to characterise the spatial patterns of recreational fishing catch rates.

Eric N Aidoo1, Ute Mueller1, Pierre Goovaerts2, Glenn A Hyndes3.   

Abstract

Western Australians are heavily engaged in recreational fishing activities with a participation rate of approximately 30%. An accurate estimation of the spatial distribution of recreational catch per unit effort (catch rates) is an integral component for monitoring fish population changes and to develop strategies for ecosystem-based marine management. Geostatistical techniques such as kriging can provide useful tools for characterising the spatial distributions of recreational catch rates. However, most recreational fishery data are highly skewed, zero-inflated and when expressed as ratios are impacted by the small number problem which can influence the estimates obtained from the traditional kriging. The applicability of ordinary, indicator and Poisson kriging to recreational catch rate data was evaluated for three aquatic species with different behaviours and distribution patterns. The prediction performance of each estimator was assessed based on cross-validation. For all three species, the accuracy plot of the indicator kriging (IK) showed a better agreement between expected and empirical proportions of catch rate data falling within probability intervals of increasing size, as measured by the goodness statistic. Also, indicator kriging was found to be better in predicting the latent catch rate for the three species compared to ordinary and Poisson kriging. For each species, the spatial maps from the three estimators displayed similar patterns but Poisson kriging produced smoother spatial distributions. We show that the IK estimator may be preferable for the spatial modelling of catch rate data exhibiting these characteristics, and has the best prediction performance regardless of the life history and distribution patterns of those three species.

Entities:  

Keywords:  Catch rate estimation; Indicator kriging; Kriging estimators; Ordinary kriging; Poisson kriging

Year:  2015        PMID: 26120221      PMCID: PMC4479307          DOI: 10.1016/j.fishres.2015.03.013

Source DB:  PubMed          Journal:  Fish Res        ISSN: 0165-7836            Impact factor:   2.422


  5 in total

1.  A comparison of multiple indicator kriging and area-to-point Poisson kriging for mapping patterns of herbivore species abundance in Kruger National Park, South Africa.

Authors:  Ruth Kerry; Pierre Goovaerts; Izak P J Smit; Ben R Ingram
Journal:  Int J Geogr Inf Sci       Date:  2013       Impact factor: 4.186

2.  Application of indicator kriging to the complementary use of bioindicators at three trophic levels.

Authors:  Rui Figueira; Paula C Tavares; Luís Palma; Pedro Beja; Cecília Sérgio
Journal:  Environ Pollut       Date:  2009-05-28       Impact factor: 8.071

3.  AUTO-IK: a 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences.

Authors:  P Goovaerts
Journal:  Comput Geosci       Date:  2009-06       Impact factor: 3.372

4.  Using indicator kriging for the evaluation of arsenic potential contamination in an abandoned mining area (Portugal).

Authors:  I M H R Antunes; M T D Albuquerque
Journal:  Sci Total Environ       Date:  2012-12-05       Impact factor: 7.963

5.  Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging.

Authors:  Pierre Goovaerts
Journal:  Int J Health Geogr       Date:  2005-12-14       Impact factor: 3.918

  5 in total
  2 in total

1.  POISSON COKRIGING AS A GENERALIZED LINEAR MIXED MODEL.

Authors:  Lynette M Smith; Walter W Stroup; David B Marx
Journal:  Spat Stat       Date:  2019-12-13

2.  Digital mapping of soil texture in ecoforest polygons in Quebec, Canada.

Authors:  Louis Duchesne; Rock Ouimet
Journal:  PeerJ       Date:  2021-06-23       Impact factor: 2.984

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.